Riley Goodside: The Art and Craft of Prompt Engineering

In episode 75 of The Gradient Podcast, Daniel Bashir (twitter.com/spaniel_bashir) speaks to Riley Goodside (twitter.com/goodside?s=20).
Riley is a Staff Prompt Engineer at Scale AI. Riley began posting GPT-3 prompt examples and screenshot demonstrations in 2022. He previously worked as a data scientist at OkCupid, Grindr, and CopyAI.
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Outline:
* (00:00) Intro
* (01:37) Riley’s journey to becoming the first Staff Prompt Enginer
* (02:00) data science background in online dating industry
* (02:15) Sabbatical + catching up on LLM progress
* (04:00) AI Dungeon and first taste of GPT-3
* (05:10) Developing on codex, ideas about integrating codex with Jupyter Notebooks, start of posting on Twitter
* (08:30) “LLM ethnography”
* (09:12) The history of prompt engineering: in-context learning, Reinforcement Learning from Human Feedback (RLHF)
* (10:20) Models used to be harder to talk to
* (10:45) The three eras
* (10:45) 1 - Pre-trained LM era—simple next-word predictors
* (12:54) 2 - Instruction tuning
* (16:13) 3 - RLHF and overcoming instruction tuning’s limitations
* (19:24) Prompting as subtractive sculpting, prompting and AI safety
* (21:17) Riley on RLHF and safety
* (24:55) Riley’s most interesting experiments and observations
* (25:50) Mode collapse in RLHF models
* (29:24) Prompting models with very long instructions
* (33:13) Explorations with regular expressions, chain-of-thought prompting styles
* (36:32) Theories of in-context learning and prompting, why certain prompts work well
* (42:20) Riley’s advice for writing better prompts
* (49:02) Debates over prompt engineering as a career, relevance of prompt engineers
* (58:55) Outro
Links:
* Riley’s Twitter (twitter.com/goodside) and LinkedIn (linkedin.com/in/goodside)
* Talk: LLM Prompt Engineering and RLHF: History and Techniques (exchange.scale.com/home/events/llm-prompt-engi…)

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